SU‐G‐TeP3‐12: Retrospective Assessment of R2star Using Ultra‐High Field MRI in a Rodent Model of Radiation Necrosis
Bibliographic record
Abstract
Purpose: To establish a quantitative MRI method that would be capable of predicting radiation necrosis without using a contrast agent. Methods: Healthy male Fischer 344 rats were irradiated using an animal irradiator capable of delivering 2.3 Gy/min with a kVp of 225 V. A dose of 40 Gy was given to half the brain in a single session. Rats were scanned using a 9.4 T animal MRI before irradiation and every two weeks following radiation until either necrosis developed or they were sacrificed for health reasons. A multi‐echo gradient‐echo sequence was acquired at every time point and the apparent transverse relaxation rate R2* was calculated based on the measured signal decay. At the last time point, an ROI mask with an R2* value of greater than 45 s−1 was applied in the area of the external and internal capsule where radiation necrosis was confirmed. A retrospective analysis was performed to determine whether R2* values would be able to predict where radiation necrosis would occur. Results: Radiation necrosis was morphologically visible between weeks 22–24 following treatment. Gadolinium MRI and histology confirmed radiation necrosis in the area of MRI enhancement. Our data suggests that there is a trend towards significance in the lesion as early as 12 weeks prior to morphological changes on MRI with significance occurring 6 weeks prior (p≤0.05, p≤0.01 at week 24). Measurements of the R2* in the hippocampus did not show any significant difference; however, there are areas of visible R2* change within sub‐hippocampal regions. Conclusion: R2* is a promising method that could be able to predict an underlying disease process that occurs prior to radiation necrosis. The constant increase in R2* values suggests a possible neuroinflammatory mechanism rather than an acute vascular event where R2* would tend to decrease in the area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".